Human2Dex：基于手腕为中心的人类演示的灵巧机器人学习
Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations
AI总结：
Human2Dex以手腕为中心的人类演示为可复用接口，通过适配与GPIR实现跨灵巧手学习，在6个任务中较Human-Raw大幅提升成功率，验证了该方法的有效性。
摘要：
为每个机器人手收集灵巧操作演示需要重复进行特定任务的数据采集，本文探究是否可以用一份共享的自然人类演示语料库作为不同灵巧手的可复用数据接口，无需在每个目标机器人上收集任务演示。本文提出Human2Dex，一种以手腕为中心的人类演示接口，它将共享的人类观察和运动语义与特定于 embodiment 的执行分离开来。该接口通过局部手部外观适配和共享的21点手部表示来对齐观察结果，该表示在人类和机器人观察中具有一致的结构语义。它通过将融合的人类手部运动重定向到每个目标手来构建可执行的监督，并引入了Grasp-Pocket Interaction Representation（GPIR，抓握口袋交互表示），以相对于以手为中心的功能抓握口袋来表达被跟踪的物体。该语料库被转换为Linker O6和Wuji的单独训练集，而动作标签、归一化、训练以及扩散策略检查点仍为特定于手的。在六个现实世界任务中，Human2Dex在Linker O6上实现了109/120（90.8%）的成功率，在Wuji上实现了97/120（80.8%）的成功率；Human-Raw在两只手上的总成功率为63/240（26.3%），而完整的Human2Dex配置的总成功率达到206/240（85.8%）。这些结果支持将以手腕为中心的人类演示用作经过校准的灵巧手之间的可复用接口，同时保留特定于 embodiment 的策略学习。该项目网站可在此URL公开获取。
英文摘要：
Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus of natural human demonstrations can instead serve as a reusable data interface for different dexterous hands, without collecting task demonstrations on each target robot. We present Human2Dex, a wrist-centric human demonstration interface that separates shared human observation and motion semantics from embodiment-specific execution. It aligns observations through local hand-appearance adaptation and a shared 21-point hand representation with consistent structural semantics across human and robot observations. It constructs executable supervision by retargeting fused human hand motion to each target hand, and introduces the Grasp-Pocket Interaction Representation (GPIR) to express tracked objects relative to a hand-centered Functional Grasp Pocket. The corpus is converted into separate training sets for Linker O6 and Wuji, while action labels, normalization, training, and diffusion-policy checkpoints remain hand-specific. Across six real-world tasks, Human2Dex achieves 109/120 (90.8%) successes on Linker O6 and 97/120 (80.8%) on Wuji. Across both hands, Human-Raw achieves 63/240 (26.3%) successes, whereas the complete Human2Dex configuration achieves 206/240 (85.8%). These results support the use of wrist-centric human demonstrations as a reusable interface across calibrated dexterous hands while retaining embodiment-specific policy learning. The project website is publicly available at https://human2dex.pages.dev/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
